In recent years, magnetic nanostructures have been widely used and researched for memory applications, but only non-volatility through remanence has been harnessed. The magnetic interaction between neighboring magnets has also been exploited for traditional Boolean computing. There has been other significant research in the field of non-Boolean computing using nanomagnets. For example, all-spin logic in device for processing and storing information and non-Boolean computing logic using spin-torque oscillators has been proposed.
The field of nanomagnetism has recently attracted tremendous attention as it can potentially deliver low-power, high-speed and dense non-volatile memories. It is now possible to engineer the size, shape, spacing, orientation and composition of sub-100 nm magnetic structures. This has spurred the exploration of nanomagnets for unconventional computing paradigms.
Many computer vision applications, including motion segmentation, correspondence, figure-ground segmentation, clustering, grouping, subgraph matching and digital graph matching require solving quadratic optimization problems. Solutions to the automated recognition of objects from an image typically involve three steps: (1) feature extraction, (2) perceptual organization and (3) object matching. Although there are many hardware solutions to speed up the first step, the perceptual organization and object matching steps are still solved by software and conventional computation involving simulated annealing or graph-cut based solutions. These vision problems place high demand on conventional computational resources, such as Boolean logic based computing platforms, and numerous clock cycles to arrive at a solution.
Accordingly, what is needed in the art is a system and method for solving complex quadratic optimization problems in fewer clock cycles than that required for traditional conventional computational resources.